Package meka.classifiers.multilabel

Interface Summary

Interface	Description
IncrementalMultiLabelClassifier	Interface for incremental multi-label classifiers.
MultiLabelClassifier	Interface for multi-label classifiers.
MultiLabelClassifierThreaded	Interface for multi-label classifiers.
MultiTargetCapable	MultiTargetCapable.java - A multi-label Classifier that can also handle generic multi-target data.
SemisupervisedClassifier	SemisupervisedClassifier.java - An Interface for Multilabel Semisupervised Classifiers.

Class Summary

Class	Description
AbstractMultiLabelClassifier	A Multilabel Classifier.
BCC	BCC.java - Bayesian Classifier Chains.
BPNN	BPNN.java - Back Propagation Neural Network.
BR
BRq	BRq.java - Random Subspace ('quick') Version.
CC	CC.java - The Classifier Chains Method.
CCq	The Classifier Chains Method - Random Subspace ('quick') Version.
CDN	CDN.java - Conditional Dependency Networks.
CDT	CDT.java - Conditional Dependency Trellis.
CT	CT - Classifier Trellis.
DBPNN	DBPNN.java - Deep Back-Propagation Neural Network.
Evaluation	Evaluation.java - Evaluation functionality.
FW	FW.java Four-class pairWise classification.
HASEL	HASEL - Partitions labels into subsets based on the dataset defined hierarchy.
LabelTransformationClassifier	Abstract label transformation classifiers, all classes that transform the labels should inherit from this classifier.
LC	LC.java - The LC (Label Combination) aka LP (Laber Powerset) Method.
MajorityLabelset	MajorityLabelset.java - The most simplest multi-label classifier.
Maniac	Maniac - Multi-lAbel classificatioN using AutoenCoders.
MCC	MCC.java - CC with Monte Carlo optimisation.
MLCBMaD	MLC-BMaD - Multi-Label Classification using Boolean Matrix Decomposition.
MULAN	MULAN.java - A wrapper for MULAN classifiers MULAN.
PCC	PCC.java - (Bayes Optimal) Probabalistic Classifier Chains.
PLST	PLST - Principal Label Space Transformation.
PMCC	PMCC.java - Like MCC but creates a population of M chains at training time (from Is candidate chains, using Monte Carlo sampling), and uses this population for inference at test time; If you are looking for a 'more typical' majority-vote ensemble method, use something like EnsembleML or BaggingML with MCC.
ProblemTransformationMethod	MultilabelClassifier.java - A Multilabel Classifier.
PS	PS.java - The Pruned Sets Method.
PSt	PSt.java - Pruned Sets with a a threshold so as to be able to predict sets not seen in the training set.
RAkEL	RAkEL.java - Draws M subsets of size k from the set of labels, and trains PS upon each one, then combines label votes from these PS classifiers to get a label-vector prediction.
RAkELd	RAkELd - Takes RAndom partition of labELs; like RAkEL but labelsets are disjoint / non-overlapping subsets.
RT	RT.java - The 'Ranking + Threshold' classifier.